POS0659 LONG-TERM DYNAMICS OF ANTIBODY RESPONSE TO ADALIMUMAB DETECTED WITH A DRUG TOLERANT ASSAY
Authors: S. Atiqi, M. Leeuw, F. Hooijberg, L. Boekel, F. Loeff, K. Bloem, C. Krieckaert, A. De Vries, M. Nurmohamed, T. Rispens, G. J. Wolbink
Journal: Annals of the Rheumatic Diseases (2022)

Background Immunogenicity of adalimumab (ADL) has been the subject of extensive research, with the primary focus on its incidence, antibody titers and effects on clinical outcome. However, the temporal evolution of antibodies, i.e. dynamic and variation in titers, time point of emergent and persistence or transience of the response, remains under elucidated. To investigate this further, it is essential to collect samples at regular intervals and over a longer period of time. Also, a drug tolerant assay should be used to conquer with the phenomenon of drug interference (1). Objectives To evaluate the temporal evolution and to distinguish dynamic patterns of antibody response. Secondly, to assess the clinical impact and factors influencing these dynamic patterns. Methods ADA and adalimumab concentration were measured in sera of 511 consecutive ADL treated rheumatoid arthritis patients. Serum samples were drawn at week 0, 4, 16, 28, 52, 78 and 104. ADA were measured with a drug tolerant assay (Acid dissociation RadioImmunoAssay). Logistic regression analysis was carried out. Benjamini-Hochberg was used to correct for multiple testing. Results Baseline characteristics are depicted in Table 1. Fifty-nine percent of patients (n=300) developed ADA. Based on visual observations patients were clustered in 9 groups, taking the next features in to account (Figure 1); the presence or absence of ADA, the height of ADA titers (higher or lower than 100AU/mL), emergent (early vs. late; cutoff week 28) and its persistence. Based on these features 397 (77%) patients were assigned to one of the groups. Due to missing data at crucial time points the remainder of patients were excluded. In the ‘High early’ and High early LTF’ group was the rate of MTX use (adjusted odds ratio (AOR) 0.033 [95%CI 0.01-0.09] P&lt;0.0001 respectively 0.79 [95%CI 0.03-0.22] P &lt;0.0001), adalimumab concentration above 5mg/L (AOR 0.022[95%CI 0.01-0.08] P&lt;0.001 respectively 0.026 [95%CI 0.01-0.09] P&lt;0.001) and low disease activity (DAS28 &lt;3.2) at week 52 (AOR 0.191[95%CI 0.07-0.56] P&lt;0.002 respectively 0.102 [95%CI 0.03-0.31] P&lt;0.001) significantly lower, compared to the negative group. Furthermore, the failure rate was in both groups significantly higher (AOR 9.19 [95%CI 3.7-22.87] P&lt;0.0001 respectively 23.94 [95%CI 8.13-70.53] P&lt;0.0001). In contrast to forgoing studies, our data does not show any differences in clinical outcomes between groups with persistent and transient ADA response. Table 1. Baseline characteristics Totaal N=511 Follow-up Median weeks (IQR) 78 (28-104) Demographics Age mean SD 53,7; ± 12,5 Female No (%) 409 (79.8) Disease status Disease duration years median (IQR) 6,7 (3-13) IgM rheumatoid factor + (%) 327 (68.2) Anti-citrullinated protein antibody + (%) 315 (73.2) Erosive (%) 296 (61.6) DAS28-score mean SD 4.5 ± 1.5 Medication Methotrexate use no (%) 378 (73) Conclusion The majority of patients have an immune response to ADL. Based on ADA concentration, time point of emergence and its persistence, certain patterns of ADA response can be distinguished. Only high ADA concentration at early time points, causing low ADL concentration, are associated with unfavorable clinical effects. All the remaining distinctive patterns does not have any association with clinical outcomes. This suggests a regulated immune response in the majority of patients. References [1]Atiqi S, Hooijberg F, Loeff FC, Rispens T, Wolbink GJ. Immunogenicity of TNF-Inhibitors. Front Immunol. 2020 Feb 26;11:312. doi:10.3389/fimmu.2020.00312. PMID: 32174918; PMCID: PMC7055461 Disclosure of Interests Sadaf Atiqi: None declared, Maureen Leeuw: None declared, Femke Hooijberg: None declared, Laura Boekel: None declared, Floris Loeff: None declared, Karien Bloem: None declared, Charlotte Krieckaert: None declared, Annick de Vries: None declared, Michael Nurmohamed Speakers bureau: AbbVie, Bristol-Myers Squibb, Eli Lilly, Roche, and Sanofi, Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, MSD, Mundipharma, Novartis, Pfizer, Roche, and Sanof, Theo Rispens Speakers bureau: AbbVie, Pfizer, and Regeneron, Grant/research support from: Genmab, Gert-Jan Wolbink Speakers bureau: UCB, Pfizer, AbbVie, Biogen and BMS, Grant/research support from: Pfeizer (paid to institution)